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9 April 2026

Development of an Assay for C13-Norisoprenoid Analysis in Riesling Wine and Its Application to Simulated Aging by Acidic Hydrolysis Using Response Surface Methodology

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Institute of Food Chemistry, Technische Universität Braunschweig, Schleinitzstrasse 20, 38106 Braunschweig, Germany
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Author to whom correspondence should be addressed.
Analytica2026, 7(2), 29;https://doi.org/10.3390/analytica7020029
This article belongs to the Section Sample Pretreatment and Extraction
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Abstract

C13-Norisoprenoids are important contributors to the aroma of Riesling wine. Their quantification is analytically challenging due to their low concentrations, the lack of commercial standards and their pronounced sensitivity to analytical conditions, reflecting their chemical lability, as well as the dynamic nature of the wine matrix, leading to high reactivity and, consequently, remarkable structural diversity. Here, we developed an assay for the analysis of C13-norisoprenoids in wine using headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME–GC-MS/MS). After evaluating different fiber materials, a statistical design of experiments (DoE) approach was employed to systematically optimize key HS-SPME parameters, including incubation, extraction and desorption conditions. Selected reaction monitoring (SRM) transitions were established for all targeted C13-norisoprenoids, allowing the assay to provide relative quantification of more than 40 compounds using representative labeled and unlabeled standards to generate linear calibration curves. Following method validation, this approach was applied to a young German Riesling wine to investigate the effect of various acidic hydrolysis conditions on the norisoprenoid profile as well as on specific compounds. A central composite design (CCD) was used to systematically study the impact of pH, temperature, and hydrolysis time. Quantitative data were obtained for 22 C13-norisoprenoids demonstrating that hydrolysis conditions strongly affected the norisoprenoid composition. pH and temperature showed a greater influence than reaction time. Response surface models (RSM) indicated that TDN, Vitispirane and TPB in particular are predominantly formed under strongly acidic and high-temperature conditions, whereas others such as Riesling acetal and actinidols are formed under milder conditions. The results indicate that hydrolysis conditions should be tailored to the specific norisoprenoid under investigation and the research question, particularly when simulating conditions of accelerated wine ageing for analytical purposes.

1. Introduction

Aroma compounds originating from carotenoid degradation, such as C13-norisoprenoids, play a key role in the sensory quality of wine [1]. These compounds are structurally diverse and include important representatives such as 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN), β-damascenone, and vitispiranes, which are particularly important contributors to the aroma profile of Riesling wines [2]. Quantification of C13-norisoprenoids requires efficient sample preparation and highly selective analytical techniques due to their occurrence at low concentrations [3,4]. A range of sample preparation techniques have been employed for the analysis of C13-norisoprenoids in wine, including distillation, liquid–liquid extraction (LLE) [5], solid-phase extraction (SPE) [6], dynamic headspace extraction and headspace solid-phase microextraction (HS-SPME) [4,7]. However, extensive sample preparation may cause losses of volatile compounds or selective discrimination against certain analytes. In recent years, HS-SPME coupled with GC-MS/MS has gained popularity for investigating the formation potential of C13-norisoprenoids [8,9,10,11]. Compared to liquid-based extraction methods, HS-SPME offers several advantages: it is solvent-free, cost-effective, rapid, easy to handle, and suitable for complex matrices like wine [12]. Various fiber coatings can be employed for HS-SPME analysis, each differing in chemical properties, resulting in varying adsorption behavior of analytes. The most commonly used stationary phases include polydimethylsiloxane (PDMS), polyacrylate (PA), divinylbenzene (DVB), carboxen (CAR), and their combinations (CAR/PDMS, DVB/CAR/PDMS, PDMS/DVB) [13]. For the extraction of C13-norisoprenoids, either individually or in combination with other volatile aroma compounds, the DVB/CAR/PDMS fiber is most frequently used [14,15,16,17,18,19,20,21]. For specific norisoprenoid targets, alternative coatings have also been applied. For instance, PDMS, DVB/PDMS, and polyethylene glycol (PEG) fibers exhibited the highest extraction efficiency for five megastigmatrienone isomers (Slaghenaufi 2021) [6], while in another study, carbowax-divinylbenzene (CW-DVB) was selected as the fiber material for a broader group of norisoprenoids [3]. In addition to fiber selection, various parameters—such as extraction time, temperature, ionic strength, sample volume, fiber conditioning, and desorption conditions—significantly influence the analytical outcome [22].
Quantification of C13-norisoprenoids should ideally be based on stable isotope dilution analysis (SIDA), which is state of the art in modern aroma analysis [23,24]. Although stable isotope-labeled standards are considered essential for accurate quantification, their commercial and synthetic availability remains limited to only a small subset of C13-norisoprenoids. As a result, most published SIDA studies focus on those few norisoprenoids for which labeled standards exist, while the majority of potential norisoprenoid analytes remain analytically inaccessible by this approach [4,24,25,26,27,28,29].
In addition, due to the complexity of wine matrices, co-elution of compounds is a frequent analytical challenge. While advanced multidimensional chromatographic techniques or elaborate sample preparation strategies can address this issue [17], selective mass spectrometric detection using selected reaction monitoring (SRM) in a triple quadrupole (QqQ) MS instrument offers an efficient alternative [24]. Despite their advantages, only a limited number of studies have applied SRM-based GC-MS/MS methods to the analysis of C13-norisoprenoids [11,24,26]. Most published methods still rely on selective ion monitoring (SIM) mode [3,18,19,29,30,31].
Beyond the analytical challenges, the occurrence of C13-norisoprenoids is strongly influenced by environmental and oenological factors such as temperature, solar radiation, and viticultural practices (e.g., defoliation, plant water status), as well as oenological factors such as yeast strain selection [29,32,33,34,35]. In warmer climates, grape berries typically contain higher carotenoid concentrations, leading to increased levels of norisoprenoids such as TDN [32,34,36]. Initially, carotenoid degradation leads to the formation of non-volatile intermediates that do not directly impact wine aroma, but act as precursors for aroma-active norisoprenoids during wine ageing. These precursors are predominantly present in glycosidically bound form in grapes, must and young wines [37,38,39]. Their conversion into free volatile compounds can occur via acid- or enzyme-catalyzed hydrolysis, as well as through photochemical reactions [40,41,42]. From an analytical standpoint, it is crucial to develop methods that enable prediction of norisoprenoid evolution during wine aging from measurements in young wine [43]. In this context, it is common practice to perform acid hydrolysis on the grapes or wine and subsequently determine the so-called aroma potential [8,29,44,45,46]. The C13-norisoprenoids group of compounds is particularly interesting in this context, as many representatives, such as TDN, become more important for the wine aroma during wine aging, and precise information about their formation during artificial wine aging is limited [19,29,45,46,47].
While several studies have investigated norisoprenoid formation, no comprehensive HS-SPME-GC-MS/MS method optimized for this compound class in Riesling wine has been established. The aim of this study was therefore the development and validation of an assay operating in SRM mode for the simultaneous, relative quantitative determination of multiple C13-norisoprenoids in Riesling wine. In contrast to previously reported approaches, the proposed method combines SRM-based detection with a systematic optimization of HS-SPME parameters using a statistical design of experiments (DoE) strategy. This allows the sensitive and selective detection of a large number of structurally related norisoprenoids across a wide concentration range. In the context of the present study, the developed method was subsequently applied to investigate the influence of acidic hydrolysis conditions—namely pH, temperature, and time—on the release and degradation behavior of C13-norisoprenoids in Riesling wine using response surface methodology (RSM). This combined methodological and mechanistic approach provides new insights into the formation behavior of aroma-active norisoprenoids during simulated wine ageing.

2. Materials and Methods

2.1. Chemicals and Materials

Authentic standard compounds β-ionone (96%), hexanal (98%), methanol (≥99.8% HPLC-grade), n-hexane (≥97% HPLC-grade), n-Alkane mixture, C8–C32 (retention index standard for gas chromatography) were obtained from Sigma-Aldrich (Steinheim, Germany). α-Ionone (90%) was obtained from Fluka (Neu-Ulm, Germany). β-Damascenone (99.54%) was obtained from Haarmann & Reimer GmbH (Holzminden, Germany). Megastigmatrienones (tabanones; 4-[butenylidene]-3,5,5-trimethylcyclo-2-hexen-1-one) as a mix of five isomers, megastigma-4,6Z,8E-trien-3-one, megastigma-4,7E,9-trien-3-one, megastigma-4,6Z,8Z-trien3-one, megastigma-4,6E,8E-trien-3-one, and megastigma-4,6E,8Z-trien-3-one, were obtained from Symrise AG (Holzminden, Germany). Acetonitrile (HPLC-grade) and sodium sulphate were obtained from VWR (Darmstadt, Germany). Acetone (≥99.8% HPLC-grade) was obtained from Fisher Scientific (Loughborough, UK). Citric acid (≥99.5% p.a.), dichloromethane (≥99.8% MS-grade), diethyl ether (≥99.8% MS-grade) di-sodium hydrogen phosphate dihydrate (≥99.5% p.a.), ethanol (≥99.95% MS-grade), pentane (≥99.8%), sodium chloride (99.8%) and sodium hydroxide (≥99.8% p.a.) were obtained from Carl Roth (Karlsruhe, Germany). Hydrochloric acid (37%) was obtained from Gruessing GmbH (Filsum, Germany). Deionized water (Nanopure® Werner GmbH, Leverkusen, Germany) was used for all experiments. All samples for chromatographic analysis were filtered through 0.2 µm PTFE filters from Agilent Technologies (Waldbronn, Germany). SPME fibers coated with 65 um of polydimethylsiloxane–divinylbenzene (PDMS–DVB), 75 µm of Carboxenpolydimethylsiloxane (CAR/PDMS), 85 µm of polyacrylate (PA), 65 µm of polydimethylsiloxane (PDMS), and 50/30 µm of divinylbenzene–carboxen–polydimethylsiloxane (DVB–CAR–PDMS) obtained from Supelco (Bellefonte, PA, USA) were used. All were thermally conditioned in accordance with the manufacturer’s recommendations.

2.2. Synthesis of Reference Compounds

The synthesis of TDN, vitispirane, β-damascenone, β-ionone, their isotopically labeled analogues, 3,4-didehydro-β-ionone, 3,4-didehydro-7,8-dihydro-β-ionol, 3,6-dihydroxy-α-ionol, 3,6-dihydroxy-7,8-dihydro-α-ionol, and 3,4-didehydro-6-hydroxy-γ-ionol was performed according to the literature and has been described previously in [48].
Other related norisoprenoid compounds used in this study, namely 3-oxo-α-ionone, 3-hydroxy-β-ionone [49,50,51], 3-hydroxy-TDN, α-ionol [52], β-ionol [52], and Riesling acetal [53], were synthesized according to published literature procedures. In each case, identity and purity were confirmed by GC-MS and NMR spectroscopy.

2.3. Generation of Reference Compounds by Simultaneous Distillation–Extraction (SDE)

In total, 50 mL of MC-Ilvaine buffer (pH: 3.2) is placed in a 100 mL round bottom flask and in each case 10 mg of the norisoprenoid standards (3,6-dihydroxy-α-jonol, 3,6-dihydroxy-7,8-dihydro-α-ionol and 3,4-didehydro-6-hydroxy-γ-ionol) is added and heated at 120 °C in an oil bath. In a 100 mL conical flask, 25 mL pentane and 25 mL diethyl ether are added and heated at 40 °C in a water bath. SDE was performed for 1.5 h as soon as both fractions have reached the upper vapor space. The organic fraction was then dried over sodium sulphate and filtered. Concentration is carried out gently using a Vigreux column. 3,6-Dihydroxy-7,8-dihydro-α-ionol was used to produce the norisoprenoids edulans, hydroxydihydroedulans, and 8-hydroxytheaspiranes. The norisoprenoids TPB, actinidols, and 4-(2,3,6-trimethylphenyl)-butan-2-one were obtained from the SDE of the compound 3,6-dihydroxy-α-ionol [48]. In addition to known C13-norisoprenoids, several previously undescribed compounds were identified by mass spectrometry in SDE extracts and were included in the method. Mass spectrometric data of these compounds are provided in Table A1 in Appendix A.

2.4. Wine Samples

For optimization of the HS-SPME-GC-MS/MS conditions, the SPME-fiber comparison (cf. Section 3.2.1) and the investigation of the hydrolysis conditions, a Riesling wine (vintage 2022) from the vineyard Leitz in Rheingau (Hessen, Germany) was used.

2.5. Analysis of Norisoprenoids

2.5.1. Sample Preparation

For the DoE investigating acid hydrolysis, 5 mL of wine with its pH value adjusted to the respective value of the DoE (pH = 1.0, 1.6, 2.4, 3.2, or 3.7) using diluted HCl or NaOH was transferred into a 20 mL headspace vial in a drying oven for the specified time and temperature. After hydrolysis was complete, the samples were immediately cooled to −22 °C until analysis. For the analysis, 2 g of sodium chloride was weighed into a headspace vial and 4.80 mL of Mc-Ilvaine buffer (pH: 7.0) was added. Then, 200 µL of the hydrolyzed wine and 50 µL internal standard mix (d6-TDN, d5-vitispiranes, d4-β-damascenone and d3-β-ionone) were added.

2.5.2. Initial HS-SPME-GC-MS/MS Parameters

Quantitation of norisoprenoids was performed on a Thermo Trace 1300 gas chromatograph (Thermo Fisher Scientific, Waltham, MA, USA) coupled to a TSQ Duo triple-quadrupole mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). An autosampler (TriPlus RSH, Thermo Fisher Scientific (Waltham, MA, USA)) with an agitator and five different SPME fibers (cf. Section 2.1) from Supelco was used to extract the volatiles from the sample vial headspace. The sample was pre-incubated for 3 min at 40 °C. Adsorption lasted 30 min, at the same temperature. Then, desorption was performed in a programmable-temperature vaporizing (PTV) inlet at 240 °C for 2 min in splitless mode and purged after 2 min. This injector was equipped with a 2.0 mm (i.d.) metal liner (Thermo Fisher Scientific, Waltham, MA, USA). The fiber penetration depth was set to 35 mm. Cleaning and conditioning of the PDMS fiber was conducted at 260 °C for 2 min under a helium flow of 1.2 mL/min in a split−splitless (SSL) inlet equipped with a 2.0 mm (i.d.) metal liner (Thermo Fisher Scientific) with a split of 1:100 before and after each sampling. Separation was achieved on an Agilent J&W VF-WAXms capillary column (Agilent Technologies, Palo Alto, CA, USA, 30 m × 0.25 mm i.d. × 0.25 µm df); carrier gas was helium and the flow rate was 1.2 mL/min. The initial oven temperature was 50 °C, held for 1 min, increased to 240 °C at 10 °C/min. Temperatures for the MS transfer line and ion source were set to 250 °C.
Mass spectra were recorded in selected reaction monitoring (SRM) mode. In SRM mode, argon (purity ≥ 99.999%) was used as the collision gas. The mass resolution was set to 1 amu for Q1 and Q3 (cycle time of 200 ms). Mass transitions (SRMs, m/z) and collision energies are listed in Table 1 and Table A2. The ions monitored in SRM runs are listed in Section 3.1 in Table 1. Data acquisition and analyses were performed using the Thermo Xcalibur version 3.0.63 (Thermo Fisher Scientific, Waltham, MA, USA) software.

2.5.3. Optimized Method Parameters

The sample was pre-incubated for 6 min at 40 °C. Adsorption lasted 45 min at the same temperature. Then desorption took place in the injector in splitless mode (2 min) at 250 °C. The fiber was then reconditioned for 2 min at 260 °C. The initial oven temperature was 40 °C, held for 4 min, and increased to 240 °C at 3 °C/min. The MS parameters were identical to those described in Section 2.5.2.
The relative quantification for the norisoprenoids was conducted as described in Section 3.3 using calibration curves for TDN, vitispiranes, β-ionone, and β-damascenone. Compounds present as isomers (actinidols and vitispiranes) were quantitated as one and referred to as one.

2.6. Statistical Analysis

The design of experiments (DoE) for the optimization of HS-SPME conditions was created and evaluated using JMP® (Version 17.0.0; SAS Institute Inc., Cary, NC, USA). The DoE for investigating acid hydrolysis was created and evaluated using Minitab (Version 22.4, LLC, State College, PA, USA). Principal component analysis (PCA) was applied to evaluate the influence of different hydrolysis conditions on the C13-norisoprenoid profile. PCA was performed using Origin 2025b version 10.25 (OriginLab Corporation, Northampton, MA, USA) software.

3. Results and Discussion

3.1. Selection of Target C13-Norisoprenoids and Auto-SRM Studies

The compounds investigated in this study were selected to represent the major norisoprenoids contributing to wine aroma, with a particular focus on those involved in the TDN formation pathway, given the pronounced relevance of TDN in Riesling wine [52,53,54,55,56]. Some compounds were not selected for their direct sensory relevance but for their proposed role as intermediates in the formation of aroma-active norisoprenoids. They can be divided into two functional categories: (i) compounds with a direct impact on the wine aroma, and (ii) compounds that primarily do not contribute directly to wine aroma but act as precursors of aroma-active norisoprenoids. By including both aroma-active compounds and their biosynthetic precursors, the method enables a comprehensive characterization of the chemical transformations shaping the aroma profile of Riesling wines during aging. The following section discusses the compounds from groups (i) and (ii), which were selected as the main focus of the method development.
(i)
Compounds with a direct impact on the wine aroma
In Riesling wines, TDN is one of the most potent aroma compounds, contributing a characteristic petrol-like note when its concentration exceeds the odor threshold, particularly in aged wines [54]. Two additional key norisoprenoids are β-damascenone and β-ionone. β-Damascenone, a ketone with an exceptionally low odor threshold of 50 ng/L in a model wine solution [57], imparts cooked apple, floral, and quince-like nuances, whereas β-ionone contributes violet, woody, and raspberry-like notes with an odor threshold of 90 ng/L in a model wine solution [1,58,59]. Isomeric vitispiranes occur as four stereoisomers that differ slightly in sensory perception but collectively evoke camphorous and eucalyptus-like impressions with an odor threshold of 800 µg/L [60,61]. Similarly, actinidols, also present as four isomers, are associated with camphoraceous, woody, and resinous descriptors [62]. Riesling acetal contributes fruity notes and is typically detected at higher concentrations in aged wines [47]. Another relevant compound, (E)-1-(2,3,6-trimethylphenyl)buta-1,3-diene (TPB), has been described as imparting green or cut-grass aromas at low concentrations [63,64]. In addition, megastigmatrienone isomers, originally identified in tobacco, but also present in both red and white wines, are associated with wine aging [65,66].
(ii)
Compounds that primarily do not contribute directly to wine aroma because of their low contents or compounds that act as precursors of aroma-active norisoprenoids
In a number of studies, hydrolysis of grape and juice extracts or norisoprenoid precursors has been shown to yield various volatile norisoprenoids, some of which are odor-active, while others serve as intermediates in the formation of major aroma compounds [37,52,53,67,68]. As a degradation product of epoxycarotenoids—primarily neoxanthin and violaxanthin—3-hydroxy-TDN is formed as one of the intermediates in the biosynthetic pathway leading to TDN. 3-Hydroxy-β-ionone and dihydrodehydro-β-ionone have been postulated as precursors of TDN, originating from the oxidative degradation of lutein [69,70,71]. Vitispirane is formed from a reduced TDN precursor, 3,6-dihydroxy-7,8-dihydro-α-ionol [72]. Along this pathway, 3,4-didehydro-7,8-dihydro-6-hydroxy-γ-ionol [56] and 8-hydroxytheaspirane isomers have been identified as further precursors of isomeric Vitispiranes [72,73]. 5,6-Epoxy-β-ionone is formed as a degradation product of β-carotene and represents a reactive intermediate from which, among other compounds, β-ionone formation is suggested [74]. Theaspirane (2,6,10,10-tetramethyl-1-oxa-spiro [4,5]-deca-6-ene) has been previously identified in wine and is formed from the precursor 4-hydroxy-7,8-dihydro-β-ionol and occurs as four stereoisomers: the enantiomeric pair A [(2R, 5R) and (2S, 5S)], described as having a camphor like aroma, and B [(2S, 5R) and (2R, 5S)], which are associated with fruity and blackcurrant notes [75,76,77,78].
Several additional norisoprenoids were detected during the hydrolytic degradation of 3,6-dihydroxy-α-ionol, 3,6-dihydroxy-7,8-dihydro-α-ionol and 3,4-didehydro-6-hydroxy-γ-ionol. These compounds occur as glycosides in grapes and primarily release actinidols and vitispiranes upon hydrolysis [48,52]. These compounds include, among others, 4-(2,3,6-trimethylphenyl)butan-2-one, 4-(2,3,6-trimethylphenyl)butan-2-ol, 4-(2,4,6-trimethylphenyl)butan-2-ol, isomeric edulans and hydroxydihydroedulans.

Selection of Analytical Standards and Extracts and Optimization of SRM Transitions

For the analysis, either commercially available standards were used (Section 2.1), in-house syntheses were carried out (Section 2.2) or extracts were obtained using simultaneous distillation extraction (SDE) (Section 2.3). In addition to known C13-norisoprenoids, several unidentified compounds were detected by mass spectrometry in SDE extracts and were included in the method. Their mass spectrometric features were consistent with norisoprenoid-like structures and, according to previous in-house investigations [48], some of these compounds may be involved in pathways related to TDN formation. In the SDE extracts of the compounds 3,6-dihydroxy-α-ionol and 3,4-didehydro-6-hydroxy-γ-ionol, the compounds unknown_3 (MW 172) and unknown_4 (MW 172) were characterized, each showing a mass spectrometric profile similar to TDN. Furthermore, three additional compounds (unknown_5 (MW 190); unknown_6 (MW 190) und unknown_7 (MW 190)) with a molecular ion at m/z 190 were detected in the same extract. In the SDE extract of 3,6-dihydroxy-α-ionol, two additional compounds (unknown_1 (MW 172) and unknown_2 (MW 172)) eluting directly before TPB exhibited mass spectra comparable to TDN and TPB, suggesting structurally related norisoprenoid derivatives. Mass spectrometric data of these compounds are provided in Table A1 in Appendix A.
MS/MS optimization was carried out for all compounds and data were acquired in SRM. The determination was made by acquiring two or three MS/MS transitions for each compound. The precursor ion was isolated in the first quadruple and further fragmented in the collision cell with different collision energy values (5, 10, 15, and 20 eV) to obtain the corresponding product ions with the optimal collision energy values. The most intense product ion was selected as the quantifier (Q), while the second most intense ion served as the qualifier (q). Confirmation of the identity of the compounds in samples was based on q/Q ratios in samples and reference standards. The maximum tolerance accepted was based on the guidelines in the document SANTE/11312/2021v2 used for the validation of pesticide analysis [79]. This, together with the retention time, provided unequivocal recognition of the compounds. Table 1 provides an overview of the target norisoprenoids for which SRM data have been recorded. Table A2 in Appendix A provides equivalent data for additional compounds that were also acquired through the auto-SRM studies.
Table 1. Retention time, SRM transitions and CE of the target norisoprenoids.

3.2. Optimization of HS-SPME-GC Conditions Using Statistical Design of Experiments (DoE)

3.2.1. SPME Fiber Selection

Different fiber materials and dimensions were evaluated for their ability to qualitatively and quantitatively extract a broad range of C13-norisoprenoids without discriminating individual compounds. For this purpose, each fiber type was tested in triplicate under identical extraction conditions (Section 2.5.2) using the same hydrolyzed wine sample. The summed signal intensities of all monitored SRM transitions were used to compare overall fiber performance. As shown in Figure 1, PDMS exhibited the highest extraction efficiency, followed by the CAR/PDMS mixed fiber. In contrast, the commonly used DVB/CAR/PDMS fiber showed significantly lower extraction efficiency [14,15,16,17,18,19,20,21]. Thus, PDMS was selected for further method optimization.
Figure 1. Comparison of the SPME fiber types based on the summed areas of the norisoprenoids.

3.2.2. Selection of the Key Factors of the Extraction Process Using HS-SPME

For HS-SPME, key factors include the efficient extraction of target compounds from the sample matrix and their complete desorption for gas chromatographic analysis. Several parameters influence the extraction process, including extraction temperature and time, solvent volume and sample matrix [81]. The DoE was applied to optimize the extraction process and improve method sensitivity. To model the interactions of the factors efficiently and with minimal experimental effort, a Box–Behnken design was chosen [22,82]. The main parameters to be analyzed were selected based on literature reports and are discussed below.
The addition of sodium chloride increases the ionic strength of the sample, reduces the solubility of many compounds, and promotes the transfer of volatiles into the headspace [22]. Salt concentration was not included as a factor in the DoE, as saturation of the solution is typically sufficient to achieve optimal extraction [83]. The sample pH can affect extraction efficiency, particularly for acidic and basic compounds [84]. In wine analysis, lower pH values yielded slightly poorer results than neutral or mildly basic conditions [65]. As the effect appears minor, a neutral pH 7.0 buffer was used as the sample matrix. Extraction temperature is the most commonly adjusted factor, typically ranging from ~30 °C (technical limit) to 50–60 °C in wine analysis [83,85,86]. Since adsorption of analytes onto the fiber coating is an exothermic process, higher temperatures facilitate their release from the matrix but simultaneously reduce adsorption due to a decreased partition coefficient. As a result, higher temperatures may ultimately lower extraction efficiency at equilibrium [81,87,88]. Extraction time is a critical factor, especially when establishing routine methods with high sample throughput. In the literature, the extraction time is tested between 10 and 60 min [22,83,85,86].

3.2.3. Experimental Design and Selection of Response Variables

The compounds TDN, vitispiranes, β-damascenone, edulans, TPB, Riesling acetal and actinidols were selected as representative analytes for the chemically heterogeneous group of norisoprenoids. These substances are among the most important representatives of this substance class in Riesling wine and cover a broad structural diversity, allowing the results to be extrapolated to other norisoprenoids. An authentic German Riesling wine was used for the analyses, which had been previously subjected to acid hydrolysis. Acidic hydrolysis under moderate conditions leads to acid-catalyzed release of various norisoprenoids from glycosidic precursor compounds and is used in various studies as a form of artificial wine aging as described in the Introduction. By increasing the concentrations of most target compounds, hydrolysis provides a suitable matrix for method optimization measurements.
The influence of extraction time (tex), extraction temperature (Tex) desorption temperature (Tde), incubation time (tin) on SPME efficiency was investigated using a Box–Behnken experimental design. The selected lower and upper levels for each factor are summarized in Table 2, and three replicate measurements were performed at the central point of the experimental domain. Experiments were randomized to minimize the impact of extraneous factors and reduce unexplained variability in the responses.
Table 2. Variables and actual and coded levels employed in the Box–Behnken design to optimize the extraction conditions.
The results of the DoE are summarized in Table 3 and response surface models are shown in Figure 2. Model quality was evaluated using the coefficient of determination (R2) and adjusted R2, which accounts for the number of parameters relative to observations [89]. For all compounds, R2 values exceeded 0.87, while adjusted R2 values were above 0.69, indicating robust model fits. Pareto diagrams were used to rank the effects according to their magnitude, with Table 3 listing the five strongest effects in order of importance. As summarized in Table 3, desorption temperature (Tde) had the strongest impact on the signal intensity of all target compounds, followed by extraction time (tex) and extraction temperature (Tex). Significant quadratic effects (Tex2 and Tde2) and interactions (Tex × Tde) indicate non-linear extraction behavior. Incubation time (tin) exerted a comparatively minor influence, although it was statistically significant for edulans, Riesling acetal, actinidols, and β-damascenone. RSM plots revealed that increasing extraction time and temperature generally enhanced analyte responses, while desorption temperature exhibited an optimum at 250 °C for all compounds within the investigated range. The presence of significant interactions and quadratic terms underscores that simple linear adjustments would be insufficient to achieve optimal extraction, justifying the application of a DoE approach.
Table 3. Statistical significance of the studied effects of the HS-SPME extraction. Experiments and data analysis were performed using JMP.
Figure 2. Response surfaces as functions of extraction time vs. extraction temperature.
Based on these findings, HS-SPME conditions were set for subsequent analyses as follows (Section 2.5.3): extraction time of 45 min, extraction temperature of 40 °C, incubation time of 6 min, and desorption temperature of 250 °C. These settings provided a balanced compromise, maximizing extraction efficiency for the selected norisoprenoids while maintaining method robustness and reproducibility.

3.3. Quantitative Approach and Analytical Performance Parameters

For most of the C13-norisoprenoids analyzed, no commercial standards are available. Therefore, a relative quantitative approach was applied. The quantification strategy followed the SRM-based approach described previously by Gök et al. 2019 [24]. TDN, vitispiranes, β-damascenone, and β-ionone were used as representative standards, and their isotopically labeled analogues (d6-TDN, d5-vitispiranes, d4-β-damascenone and d3-β-ionone) served as internal standards. Calibration curves were established for each of the 4 standard compounds. Calibration curves were obtained using linear regression, plotting the response ratio (analyte peak area/internal standard peak area) against the concentration ratio (analyte added concentration/internal standard concentration). For norisoprenoids lacking authentic reference standards, concentrations were estimated using the internal standard and the calibration curve of the chemically most similar reference compound. As compound-specific response factors were not determined, the obtained values represent semi-quantitative estimates that allow relative comparison of the analyte levels across different samples. The respective standard applied for relative quantification for each analyte is listed in Table 1.
Method validation of the HS-SPME-GC-MS/MS assay was performed in terms of the limit of detection (LOD), limit of quantification (LOQ), and linearity range. The results are summarized in Table 4.
Table 4. Method validation parameters.
The LOQ was taken as the lowest point of the calibration curve and the LOD was set at 1/3 times the LOQ. The linearity of the method was achieved by injecting each compound at 11 different concentration ranges as listed in Table 4. The linear calibration parameters were obtained using the least squares regression method. The squared correlation coefficient (R2) was used to estimate linearity. R2 was in a range from 0.9967 to 0.9979 for all four compounds and indicated good fit and linearity for the calibration curves, in relation to the scope of the method. Since the values of the analyzed compounds are distributed over a range of more than 2 decades, two calibration curves are created for TDN and vitispiranes.

3.4. Investigation of Norisoprenoid Profiles Under Different Hydrolysis Conditions in Riesling Wine

3.4.1. Experimental Design for Acidic Hydrolysis Conditions

In wine, a large proportion of aroma-active compounds occurs in glycosidically bound form [38,68,90]. These glycosides can be subdivided into ‘glycosides of aroma molecules’ and ‘glycosides of precursors of aroma molecules’ [43]. The former can be effectively analyzed by enzymatic hydrolysis, whereas many aroma-active norisoprenoids are formed from the pool of ‘glycosides of precursors of aroma molecules’ requiring acid-catalyzed rearrangements of the precursors for their formation. To simulate this process, artificial aging methods are frequently employed to predict norisoprenoid formation from these precursors. Previous studies demonstrated that individual parameters, particularly temperature, strongly influence norisoprenoid formation. At 50 °C, an increase in TDN and β-damascenone concentrations was observed after 2.5 weeks, and TDN continued to increase significantly, albeit at a slower rate, over the following 2.5 weeks [11]. In red wine, β-damascenone and 3-oxo-α-ionol reach maximum concentrations after 48–72 h at 60 °C, whereas TPB and megastigmatrienone isomers increase linearly with aging time [19]. A further study at 50 °C confirms that increased norisoprenoid formation takes place in the first 7 days, including Riesling acetal, TDN and TPB, while a continuous increase over 9 weeks was observed for actinidols [91]. 3-Oxo-α-ionol is described in some cases as continuously increasing [91] and in others as reaching a plateau after a few days [19]. In the case of Riesling acetal, vitispiranes, TDN, TPB and β-damascenone, the concentration decreased again after approximately 1 week under model conditions, due to a low concentration of precursors and low stability of the norisoprenoids in the experimental medium [91]. β-Ionone, by contrast, reaches a constant concentration after the increase in the first week. Overall, temperature has a significant influence on the speed at which wine aroma develops. Storage temperature is consistently described as the parameter with the greatest influence [92,93]. A synergistic effect of elevated temperature (≥45 °C) and reduced pH has also been described, promoting the formation of TDN, 2,2,6-trimethylcyclohexanone (TCH), and vitispiranes [5]. In contrast, β-damascenone exhibits lower stability under strongly acidic conditions and rearranges into two bicyclic products [94].
The developed HS-SPME-GC-MS/MS assay was applied to systematically study the effects of hydrolysis conditions on the formation of C13-norisoprenoids in Riesling wine. To this end, a central composite design (CCD) was employed, including the factors pH, hydrolysis time, and temperature. Factor levels were selected based on established literature methods. The resulting experimental design is summarized in Table 5.
Table 5. Variables and actual and coded levels employed in the central composite design (CCD) to investigate the acid hydrolysis of the Riesling wine.

3.4.2. Results of the DoE Investigation of Acid Hydrolysis

Evaluation of the Investigated Factors
The results of the DoE are summarized in Table A4 and Table A5 in Appendix A. For the response surface models, factor elimination was applied to improve model quality. Higher-order interactions that showed no significant effect on the response variables were identified via half-normal plot and subsequently removed. Based on the model fit (R2 adj.), the models could be classified into three categories: (i) very good models with R2 adj. ≥ 0.70 (n = 13 compounds), (ii) good models with 0.50 ≤ R2 adj. < 0.70 (n = 4 compounds), and (iii) weak models with R2 adj. < 0.50 (n = 8 compounds). The results indicate that the chosen experimental design adequately described the formation of the majority of the compounds. For some analytes, however, the regression quality was weak, suggesting that their formation and degradation are governed by more complex mechanisms not fully captured by this statistical approach. As anticipated, pH was the dominant factor, significantly affecting 18 compounds. Lower pH values promoted more extensive hydrolysis of glycosidic precursors, but also accelerated the degradation of certain norisoprenoids. Temperature was another critical factor, significantly influencing most compounds. Importantly, the interaction between temperature and pH also contributed substantially to the overall profile. In contrast, hydrolysis time had a comparatively minor effect relative to pH and temperature. In summary, pH and temperature were identified as the key drivers of norisoprenoid release and degradation, while time played only a secondary role.
Impact of Hydrolysis Conditions on Individual Norisoprenoids
To illustrate the influence of the applied hydrolysis conditions on the resulting norisoprenoid profile, a principal component analysis (PCA) was performed using the norisoprenoid concentrations obtained for each DoE experimental point, as shown in Figure 3. The PCA reveals the formation of distinctly different norisoprenoid profiles across the hydrolysis experiments. Experimental points with pH values of 2.4 and above combined with lower temperatures (≤80 °C) show the highest levels of Riesling acetal. In contrast, hydrolyses performed at higher temperatures and higher pH values result in increased formation of TDN. The megastigmatrienone isomers are predominantly formed under the most extreme conditions (DoE points: pH 1.0_80 °C_12 h and pH 1.6_100 °C_18 h).
Figure 3. Principal component analysis (PCA) of the Riesling wine after different hydrolysis conditions.
For the key norisoprenoids TDN, vitispiranes, Riesling acetal, and actinidols, response surface models were generated based on the DoE results, as shown in Figure 4. These models illustrate how pH, temperature, and time individually and interactively influence the concentrations of each norisoprenoid. A low pH value significantly promoted TDN formation, in agreement with previous findings [29,47,53,95]. Temperature was also a major factor, with higher values accelerating TDN release, while hydrolysis time exerted only a minor effect. In contrast, the actinidol isomers showed the opposite trend: their concentrations were lowest at low pH values and high temperatures. Riesling acetal was preferentially formed under mild conditions, reaching its maximum at the test point 3.2_60 °C_18 h. With increasing temperature and decreasing pH, Riesling acetal concentrations declined, thus exhibiting an inverse behavior compared to TDN. In addition, longer hydrolysis times further reduced Riesling acetal levels. These observations are consistent with literature reports describing the conversion of Riesling acetal to TDN via intermediate stages, with reaction rates and equilibrium shifts being strongly favored under acidic conditions. At elevated temperatures (100 °C) and extended hydrolysis durations (7 days), an almost complete conversion of Riesling acetal into TDN has been described [47].
Figure 4. Response surface designs of the C13-norisoprenoids TDN (red), vitispiranes (bright green), Riesling acetal (dark green) and actinidols (blue), generated using Minitab.
These trends can be rationalized by the acid-catalyzed transformation of carotenoid-derived megastigmane precursors. Triol intermediates such as 3,6,9-trihydroxymegastigma-4,7-diene contain several allylic hydroxyl groups that are susceptible to acid-catalyzed rearrangements and dehydration reactions. Strauss et al. (1986) demonstrated that actinidols can be formed from this triol precursor, suggesting that actinidols may represent intermediate products within the degradation pathway [52]. Cox et al. (2005) further showed that TPB can originate from several related megastigmane triols as well as from actinidols [96]. The higher actinidol levels observed in the present study under milder conditions therefore likely reflect intermediate stages of the transformation pathway, whereas stronger acidic conditions promote further dehydration and rearrangement reactions leading to more highly transformed norisoprenoid structures such as TDN and TPB.
Use of Acidic Hydrolysis to Simulate Wine Aging
The results demonstrate that acidic hydrolysis under varying conditions can be used to induce the formation of a broad range of norisoprenoids within a short time by appropriately selecting pH and temperature. This aligns with previous findings that acid-catalyzed cleavage of glycosidic precursors can rapidly generate C13-norisoprenoids under model conditions. Approaches employing accelerated aging or “aroma potential” analysis through elevated temperature and/or acidic hydrolysis can be applied depending on the analytical objective [16]. However, our data clearly show that the choice of hydrolysis conditions has a decisive impact on the resulting norisoprenoid profile, as also indicated by studies comparing different pH and temperature regimes in models and real wines [5,16,24,29,46,97].
As several authors have emphasized, acidic hydrolysis should not be interpreted as a simple acceleration of natural wine aging [43,48]. In naturally aged wines, C13-norisoprenoids formation occurs gradually over extended periods under milder, dynamically changing conditions. Acidic hydrolysis, by contrast, promotes specific reaction pathways that favor the formation of particular compounds, yielding norisoprenoid profiles that differ from those in naturally aged wines. Consequently, no single set of hydrolysis conditions is likely to realistically mimic the complexity of natural wine aging.
Instead, simulated wine aging via acidic hydrolysis proves valuable for promoting the formation of specific norisoprenoids relevant to targeted research questions. Such methods enable relative comparisons between wines—indicating whether higher or lower amounts of target compounds form, but they should not be over-interpreted as absolute predictions of long-term bottle aging. The DoE results presented here offer practical guidance for selecting suitable hydrolysis parameters for targeted norisoprenoids, allowing acidic hydrolysis to be applied efficiently while maximizing the formation of the compounds of interest and at the same time acknowledging the conceptual distinction between analytical hydrolysis tests and genuine wine maturation.

4. Conclusions

An HS-SPME-GC-MS/MS assay was developed and successfully validated in order to relatively quantify selected norisoprenoid compounds in Riesling wine. The method development primarily included characterization of important norisoprenoids, optimization of SPME sampling, and SRM method setup.
Evaluation of different SPME fibers revealed that a PDMS fiber provides the highest extraction efficiency for targeted norisoprenoid analysis, whereas the commonly used PDMS/DVB/CAR mixed fiber showed significantly lower performance.
The method was applied to investigate changes in the norisoprenoid profile of Riesling wine under different hydrolysis conditions. Hydrolysis experiments demonstrated that the resulting profiles strongly depend on pH, temperature, and incubation time, with pH and temperature exerting the greatest influence on most compounds. Certain compounds, such as TDN, were formed at increased rates under harsh hydrolysis conditions, while others, such as Riesling acetal, exhibited an opposite trend.
This validated method provides a robust tool to investigate the formation and transformation of C13-norisoprenoids in Riesling wine, with potential applications in wine aging studies, quality control, and varietal characterization. Although the method was developed and validated using Riesling wine as a model matrix, its applicability is not limited to this grape variety. In this study, it was demonstrated that the applied methodology for simulating wine ageing is suitable for assessing relative formation trends of individual norisoprenoids between different wines. However, they do not allow the determination of absolute concentrations or the exact levels reached by individual compounds during ageing. Due to the general occurrence of carotenoid-derived norisoprenoids in grapes and other plant-based foods, this approach can be readily transferred to other grape varieties as well as to different food matrices. This underlines the versatility and broader potential of the method beyond the present case study.

Author Contributions

Conceptualization, R.G. and S.S.; methodology, R.G. and S.S.; software, S.S. and L.P.; validation, R.G. and S.S.; formal analysis, S.S. and L.P.; investigation, S.S. and L.P.; resources, P.W. and R.G.; data curation, S.S. and R.G.; writing—original draft preparation, S.S. and R.G.; writing—review and editing, S.S., R.G. and P.W. visualization, R.G. and S.S.; supervision, R.G.; project administration, R.G. and P.W. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge support by the Open Access Publication Funds of Technische Universität Braunschweig.

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CARCarboxen
CCDCentral composite design
CWCarbowax
DoEDesign of experiments
DVBDivinylbenzene
GCGas chromatography
HSHeadspace
LODLimit of detection
LOQLimit of quantification
PAPolyacrylate
PCAPrincipal component analysis
PDMSPolydimethylsiloxane
PEGPolyethylene glycol
q/QQualifier/quantifier
RSMResponse surface methodology
SDESimultaneous distillation extraction
SIDAStable isotope dilution analysis
SIMSelective ion monitoring
SPMESolid-phase microextraction
SRMSelected reaction monitoring
TDN1,1,6-trimethyl-1,2-dihydronaphthalene
TPB(E)-1-(2,3,6-trimethylphenyl)-buta-1,3-diene

Appendix A

Table A1. Mass spectrometric data of several unknown norisoprenoids, obtained from SDE extracts of norisoprenoid precursors.
Table A2. Retention time, SRM transitions and CE of additional detected norisoprenoids.
Table A3. Matrix and measurement results of the Box–Behnken experimental design for optimizing the HS-SPME-GC-MS/MS measurement.
Table A4. Response surface model regressions of norisoprenoids for the investigation of acid hydrolysis using DoE, calculated by MiniTab.
Table A5. Matrix and measurement results of the central composite experimental design investigating accelerated wine aging using acid hydrolysis.

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